Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners

Abstract

Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. The results demonstrate that SVM significantly outperforms the ID3 algorithm in vowel classification, with an average accuracy of 92.09% for the three learner groups (92.38% for advanced, 92.25% for intermediate, and 91.63% for elementary), an improvement of 2.05 percentage points compared to ID3 (p p < 0.001). This study confirms the effectiveness of objective assessment methods based on formant analysis in speech acquisition research, provides a theoretical basis for algorithm optimization in speech evaluation systems, and holds significant application value for the development of Computer-Assisted Language Learning (CALL) systems and the improvement of multi-ethnic Mandarin speech recognition technology.

Keywords

• Mandarin learners
• error analysis
• machine learning
• acoustic deviation
• pronunciation quality assessment